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强化学习的原代码还可以的 -The original source of reinforcement learning reinforcement learning can also be in the original code
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强化学习matlab源代码很少见的源代码，详细介绍Q学习的编程过程。-Reinforcement learning matlab source code，rare source code, detailed Q learning programming.
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关于增强学习的一个例子，Q-learning算法，和大家分享。-An example of reinforcement learning, and to share.
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将强化学习算法应用于Tic-tac-toe游戏-The reinforcement learning algorithm is applied to Tic-tac-toe game
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贝叶斯学习与强化学习结合技术的研
详细介绍了强化学习的算法和理论-Bayesian learning and reinforcement learning combined with the technical details of the study of reinforcement learning algorithms and theory
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reinforcement learning
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approximate reinforcement learning
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the example and toolbox for machine learning（under windows platform）including all the widely used method such as monte carlo method,reinforcement learning method and so on
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这是机器学习里面的强化学习的重要资料，主要介绍了强化学习的基本理论及实验和分析-This is inside the machine learning reinforcement learning important information, which mainly describes the basic theory of reinforcement learning and experimental and analytical
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Artificial intelligence with Reinforcement learning.
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sutton and barto books on reinforcement learning
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强化学习是人工智能中策略学习的一种，基于预期最大利益原则。和博弈论有密切的关系，也是多主体系统学习的常用方法。-Reinforcement learning is a kind of artificial intelligence in the strategic study, based on the principle of best interests is expected. And game theory are closely related, but also multi-agen
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Neural control (reinforcement learning) for tanker heading
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Multi-Agent Quadrotor Testbed Control Design:
Integral Sliding Mode vs. Reinforcement Learning
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AI Reinforcement Learning 走格子， 输出每1000步达到目标格子次数。reward: goal-> +1 rest -> 0.-Java implementation for an Reinforcement Learning agent to search through a Grid World from start point to goal state. reward: goal->+1 rest-> 0.
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基于强化学习的机器人寻路，以方格地图为环境-reinforcement learning apply in the robatics
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Reinforcement Learning
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强化学习在认知无线电领域的应用，很有价值的英文参考资料。-Reinforcement learning in the field of cognitive radio applications, valuable reference materials in English.
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Reinforcement learning, a Q learning algorithm, implementation on a robot that tryies to solve randomly created maze and reach the goal. Note that you can run .m files both on Matlab and Octave.
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在这本书中，我们探索了一种从交互中学习的计算方法。我们不直接对人或动物如何学习进行理论分析，而是探索理想化的学习情境，评估各种学习方法的效率。也就是说，我们采用人工智能研究人员或工程师的角度。我们探索去设计在这些方面上格外有效率的机器，他能够解决科学或经济学领域的问题。通过数据分析和计算实验来评估这些设计。我们将这种方法称为强化学习，更侧重于目标导向的交互学习，而不是其他方法。(In this book, we explored a computational method of learnin
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